Self-ogranizing neural networks and various euclidean traveling salesman problems

Yasuo Matsuyama

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

By using competitive learning, which causes just one or a group of a small number of neurons to respond to a given input, self-organization of entire neural networks can be achieved. When this self-organization process is applied to various kinds of traveling salesman problems in a Euclidean space, a good approximation or the true solution is obtained. We use a sequential update which looks at the position vector of each city one at a time as the training method for a neural network arranged as a closed loop. In this case, we use symmetrical connections between neurons. The number of neurons required is approximately linear in the number of cities. In the first experiment, we carried out a quantitative comparison with the simulated annealing method using 500 sets of 30 cities and demonstrated this method's superiority. Next, we obtained a good approximation on a set of 532 U.S. cities and demonstrate its superiority with respect to the increase in the number of cities in actual (realistic) data. Further for a generalized constrained multiple-salesman problem, we explain this method's compactness and efficiency and give an experimental example. The computation can be adequately performed by a common workstation with a serial processor.

Original languageEnglish
Pages (from-to)101-112
Number of pages12
JournalSystems and Computers in Japan
Volume23
Issue number2
Publication statusPublished - 1992
Externally publishedYes

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Information Systems
  • Theoretical Computer Science

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